Publications by authors named "D C J J Bergmans"
Importance: A review of the study processes and protocols afterward by the researchers themselves is scarce.
Objectives: The present study aimed to evaluate the study design and the process of data collection of the Maastricht Intensive Care COVID (MaastrICCht) cohort during the COVID-19 pandemic. This evaluation provides information about the quality of the predefined questions and contributes to transparency in science.
Article Synopsis
- There is currently no universally accepted method for titrating positive end expiratory pressure (PEEP) in patients on spontaneous mechanical ventilation (SMV), despite some success with electrical impedance tomography (EIT) in controlled mechanical ventilation.
- A new approach using regional peak flow (RPF) via EIT aims to evaluate lung mechanics specifically for SMV, which was not effectively addressed by existing algorithms.
- In a study of 25 COVID-19 ARDS patients, EIT-guided PEEP titration showed feasibility and suggested that a cumulative collapse threshold of around 5% could provide the best balance of clinical and mechanical outcomes.
Background: Lung ultrasound (LUS) in an emerging technique used in the intensive care unit (ICU). The derivative LUS aeration score has been shown to have associations with mortality in invasively ventilated patients. This study assessed the predictive value of baseline and early changes in LUS aeration scores in critically ill invasively ventilated patients with and without ARDS (Acute Respiratory Distress Syndrome) on 30- and 90-day mortality.
View Article and Find Full Text PDFBackground: Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation.
Aim: To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients.
Article Synopsis
- Researchers improved a model called Deep Embedded Clustering (DEC) to better handle different types of data, like numbers and categories.
- They created a new version called X-DEC by using a special tool (an X-shaped variational autoencoder) to make it work better.
- After testing both models on patients in intensive care, they found that while both created clear groups, X-DEC gave more consistent results.